{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用柱形图进行对比分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 初始设置\n",
    "\n",
    "首先，导入所需的库，并设置中文字体等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入所需的库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as image\n",
    "\n",
    "# 正常显示中文标签\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "\n",
    "# 自动适应布局\n",
    "mpl.rcParams.update({'figure.autolayout': True})\n",
    "\n",
    "# 正常显示负号\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 定义颜色，主色：蓝色，辅助色：灰色，互补色：橙色\n",
    "colors = {'蓝色':'#00589F', '深蓝色':'#003867', '浅蓝色':'#5D9BCF',\n",
    "          '灰色':'#999999', '深灰色':'#666666', '浅灰色':'#CCCCCC',\n",
    "          '橙色':'#F68F00', '深橙色':'#A05D00', '浅橙色':'#FBC171'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 定义数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义几个模拟的数据\n",
    "df = pd.DataFrame({'产品':['产品 A', '产品 B', '产品 C', '产品 D'], '销量':[66, 23, 26, 29]})\n",
    "\n",
    "# 画图用的数据定义\n",
    "x = df['产品']\n",
    "y = df['销量']\n",
    "\n",
    "# 平均值\n",
    "avg = y.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 开始画图\n",
    "\n",
    "用「**面向对象**」的方法画图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dXV1vOJMo0kH/Lx8vv/yyrrvuOu/3W6iDbt/M8ePH33B+mtHRUWWz2Tec5G8qhoaGVFFR4as8SWd/vM2swTm3bvLl7KKKzCCfsZa8Qn0ODS4P9B8Tj3W5VCYPN9LFfaq57+HmQhTHUgEAAAEUei8Ici7kcWbAicyc818JRJEZf5cB0kT/Lx9lZWUaHh72fr/ZbNb7fV7uhoeHz/vYJAacyHS/zkHGqZs7d27oEhAQ/b98VFVVqaenR8ePH/e6knMxu4aKjXNOx48fV09Pz5TP6TOORzEy18116j3OkJOyw4cPnzqjKNJD/y8f4306dOiQRkZGvN3vyMhIQT5d+3JVVlamq6666rz/XzDgROYnfQw3qeMdNGmj/5eXOXPmeB9IQ7yLqhixiyoym5ay7zV1Bw4cCF0CAqL/IAN+MOBEplg+hwoXjs8hShv9BxnwgwEnMjtrx0KXgMAaGhpCl4CA6D/IgB8MOJHZ3c4KTurq6upCl4CA6D/IgB8MOJFhBQf89pY2+g8y4AcDTmRYwQG/vaWN/oMM+MGAE5ltK1jBSV1TU1PoEhAQ/QcZ8IMBJzLfPkhLUrd69erQJSAg+g8y4AevppHZuIQPbktde3t76BIQEP0HGfCDAScyDf2cyTh11dXVoUtAQPQfZMAPBpzI1M5hBSd1/f39oUtAQPQfZMAPBpzI9A2zgpO6ioqK0CUgIPoPMuAHA05kZpaygpM6n59KjMsP/QcZ8IMBJzJldCR52SwfuJoy+g8y4Acvp5E5kmEXVepmzZoVugQERP9BBvxgwInM8gp2UaXu6NGjoUtAQPQfZMAPBpzI7DvKCk7qlixZEroEBET/QQb8YMCJzK2L2PeauldeeSV0CQiI/oMM+MGAE5mnumhJ6q699trQJSAg+g8y4MeUXk3N7HfN7B/M7H+ZWVX+781mtqvQBaZm+0pWcFK3d+/e0CUgIPoPMuDHOQccM/sVSaudc7dJ+l+S/kTSk5LWSPoXZraqsCWm5bGOaaFLQGA333xz6BIQEP0HGfBjKis475I0z8x+KOk2SSskfc85l5X0A0l3FbC+5NxXOxa6BATW0NAQugQERP9BBvyYyoBTKelV59ztkqolvU3SL/L/Nihp/uQbmNkDZlZvZvW9vb3q7+9Xb2+venp6NDAwoI6ODg0PD6ulpUXZbFaNjY2SftnUxsZGZbNZtbS0aHh4WB0dHRoYGFBPT4/G76+zs1NDQ0NqbW1VeYnTthW5wWBn7elfty4b0+xSp41Lslo8y2l9ZVbXXJn7s74yd9nGJVnNLnXauuzM97FtxZjKS5zurs6qcobThqqsVl7hdP28rG5akFVNhdPti7KaW+60uWZMJXLasTJ32/GBZcfK3OWba8Y0tzx3/ZoKp5sWZHX9vNz9bajK6rvdJbq7OlsU2zS5T6Ojo2pqajqt1+Nfm5ublclk1NbWpsHBQXV1damvr099fX3q6urS4OCg2tralMlk1NzcfMb7aGpq0ujoqFpbWzU0NKTOzs6CZ68Q21RXV1d021SMfSrUNs2cObPotqkY+1TIbbr66quLbpsK2aezMefe/LwrZvYRSbOcc583s69LukXS+5xzDWb2Z5L2Oee+crbbr1u3ztXX17/pz7hYyx96sqD3fyntWDlWNLupOnfdG7qEy1JjYyNL1Amj/yAD58fMGpxz6yZfPpUVnAZJ6/N/r5X0M0mbzKxE0h2SnvdWJfR4B++iSt3atWtDl4CA6D/IgB/nfDV1zr0gqd/MXlJuuPk3ku6RtE/Sk8659sKWmJZ7angXVepaW1tDl4CA6D/IgB+lU7mSc+7Dky66rQC1QNKen7OCk7oVK1aELgEB0X+QAT94NY3MjfP5LKrUHTp0KHQJCIj+gwz4wYATmc4hPosqdfPnv+GNiUgI/QcZ8IMBJzILprOCk7rjx4+HLgEB0X+QAT8YcCIzwjHGySsp4b9lyug/yIAfPIqRGR5lF1XqysrKQpeAgOg/yIAfDDiRqZrJLqrUDQ0NhS4BAdF/kAE/GHAi0z7ICk7qFi5cGLoEBET/QQb8YMCJTN1CVnBS193dHboEBET/QQb8YMCJzHOHWMFJXW1tbegSEBD9BxnwgwEnMluW8Taq1O3fvz90CQiI/oMM+MGAE5lvvFIcnySOC7dmzZrQJSAg+g8y4AcDTmR21o6FLgGBNTQ0hC4BAdF/kAE/GHAis7udFZzU1dXVhS4BAdF/kAE/GHAiwwoO+O0tbfQfZMAPBpzIsIIDfntLG/0HGfCDAScyW5exgpO65ubm0CUgIPoPMuAHA05knu6hJalbtWpV6BIQEP0HGfCDV9PIvL2KMxmnrqurK3QJCIj+gwz4wYATmZePcSbj1F111VWhS0BA9B9kwA8GnMhUz2YFJ3XHjh0LXQICov8gA34w4ERm8GToChDajBkzQpeAgOg/yIAfDDgAAKDoMOBEZk556AoQ2okTJ0KXgIDoP8iAHww4kel+nYOMUzd37tzQJSAg+g8y4AcDTmSum8tBxqk7fPhw6BIQEP0HGfCDAScyP+ljBSd1NTU1oUtAQPQfZMAPBpzIbFqaDV0CAjtw4EDoEhAQ/QcZ8IMBJzLfOsiHbabuhhtuCF0CAqL/IAN+MOBEZmctH7aZuoaGhtAlICD6DzLgBwNOZHa3s4KTurq6utAlICD6DzLgBwNOZFjBAb+9pY3+gwz4wYATGVZwwG9vaaP/IAN+MOBEZtsKVnBS19TUFLoEBET/QQb8YMCJzLcP0pLUrV69OnQJCIj+gwz4watpZDYu4UzGqWtvbw9dAgKi/yADfjDgRKahnzMZp666ujp0CQiI/oMM+MGAE5naOazgpK6/vz90CQiI/oMM+MGAE5m+YVZwUldRURG6BARE/0EG/GDAiczMUlZwUjcyMhK6BARE/0EG/GDAiUwZHUleNssHrqaM/oMM+MHLaWSOZNhFlbpZs2aFLgEB0X+QAT8YcCKzvIJdVKk7evRo6BIQEP0HGfCDAScy+46ygpO6JUuWhC4BAdF/kAE/GHAic+si9r2m7pVXXgldAgKi/yADfjDgROapLlqSumuvvTZ0CQiI/oMM+MGraWS2r2QFJ3V79+4NXQICov8gA34w4ETmsY5poUtAYDfffHPoEhAQ/QcZ8OOcA46Z3W1m3Wa2J/9njZl9x8yazGy3mXFUrEf31Y6FLgGBNTQ0hC4BAdF/kAE/prqC81+cc7c6526VtF5St3NujaR5kt5dsOoS9Gg7Kzipq6urC10CAqL/IAN+THXA+ddm9o9m9k1J75L0vfzlz0m6qyCVJWrHSlZwUtfY2Bi6BARE/0EG/JjKgNMh6T86594mabGkX5P0i/y/DUqaP/kGZvaAmdWbWX1vb6/6+/vV29urnp4eDQwMqKOjQ8PDw2ppaVE2mz3VzPFlucbGRmWzWbW0tGh4eFgdHR0aGBhQT0+Pxu+vs7NTQ0NDam1tVXmJ07YVucFgZ+3pX7cuG9PsUqeNS7JaPMtpfWVW11yZ+7O+MnfZxiVZzS512rrszPexbcWYykuc7q7OqnKG04aqrFZe4XT9vKxuWpBVTYXT7YuymlvutLlmTCVypwaV8V1OO1bmLt9cM6a55bnr11Q43bQgq+vn5e5vQ1VWz/SY7q7OFsU2Te7T6OiompqaTuv1+Nfm5mZlMhm1tbVpcHBQXV1d6uvrU19fn7q6ujQ4OKi2tjZlMhk1Nzef8T6ampo0Ojqq1tZWDQ0NqbOzs+DZK8Q2rV27tui2qRj7VKhtmj59etFtUzH2qZDbtGTJkqLbpkL26WzMuTc/c66ZLZA05JzLmNljkrZKus85900z+5ik+c65T5zt9uvWrXP19fVv+jMu1vKHnizo/V9Km2vG9J2u4thN1bnr3tAlXJZaWlr01re+NXQZCIT+gwycHzNrcM6tm3z5VFZw/p2k7WZWIul6SR+TtCn/bxslPe+tSmjPz3ljW+pWrFgRugQERP9BBvyYyqvplyT9W0k/kfQtSX8paamZ7ZN0VNKzhSsvPTfO57OoUnfo0KHQJSAg+g8y4Efpua7gnOuVdOekizcXpBqoc4h33adu/vw3HNaGhNB/kAE/2B8SmQXTWcFJ3fHjx0OXgIDoP8iAHww4kRnhkxqSV1LCf8uU0X+QAT94FCMzPMouqtSVlZWFLgEB0X+QAT8YcCJTNZNdVKkbGhoKXQICov8gA34w4ESmfZAVnNQtXLgwdAkIiP6DDPjBgBOZuoWs4KSuu7s7dAkIiP6DDPjBgBOZ5w6xgpO62tra0CUgIPoPMuAHA05ktizjbVSp279/f+gSEBD9BxnwgwEnMt94pTg+hwoXbs2aNaFLQED0H2TADwacyIx/2jfSNf6JukgT/QcZ8IMBJzK721nBSV1dXV3oEhAQ/QcZ8IMBJzKs4IDf3tJG/0EG/GDAiQwrOOC3t7TRf5ABPxhwIrN1GSs4qWtubg5dAgKi/yADfjDgRObpHlqSulWrVoUuAQHRf5ABP3g1jczbqziTceq6urpCl4CA6D/IgB8MOJF5+RhnMk7dVVddFboEBET/QQb8YMCJTPVsVnBSd+zYsdAlICD6DzLgBwNOZAZPhq4Aoc2YMSN0CQiI/oMM+MGAAwAAig4DTmTmlIeuAKGdOHEidAkIiP6DDPjBgBOZ7tc5yDh1c+fODV0CAqL/IAN+MOBE5rq5HGScusOHD4cuAQHRf5ABPxhwIvOTPlZwUldTUxO6BARE/0EG/GDAicympdnQJSCwAwcOhC4BAdF/kAE/GHAi862DfNhm6m644YbQJSAg+g8y4AcDTmR21vJhm6lraGgIXQICov8gA34w4ERmdzsrOKmrq6sLXQICov8gA34w4ESGFRzw21va6D/IgB8MOJFhBQf89pY2+g8y4AcDTmS2rWAFJ3VNTU2hS0BA9B9kwA8GnMh8+yAtSd3q1atDl4CA6D/IgB+8mkZm4xLOZJy69vb20CUgIPoPMuAHA05kGvo5k3HqqqurQ5eAgOg/yIAfDDiRqZ3DCk7q+vv7Q5eAgOg/yIAfDDiR6RtmBSd1FRUVoUtAQPQfZMAPBpzIzCxlBSd1IyMjoUtAQPQfZMAPBpzIlNGR5GWzfOBqyug/yIAfvJxG5kiGXVSpmzVrVugSEBD9BxnwgwEnMssr2EWVuqNHj4YuAQHRf5ABPxhwIrPvKCs4qVuyZEnoEhAQ/QcZ8IMBJzK3LmLfa+peeeWV0CUgIPoPMuAHA05knuqiJam79tprQ5eAgOg/yIAfvJpGZvtKVnBSt3fv3tAlICD6DzLgBwNOZB7rmBa6BAR28803hy4BAdF/kAE/GHAic1/tWOgSEFhDQ0PoEhAQ/QcZ8GPKA46Z/Y6ZPWNmC83sH8ys2cx2FbK4FD3azgpO6urq6kKXgIDoP8iAH1MacMxsmaT789/+tqQnJa2R9C/MbFVhSkvTjpWs4KSusbExdAkIiP6DDPgx1RWcP5X0H/J/3yjpe865rKQfSLqrEIWl6vEO9hqmbu3ataFLQED0H2TAj3O+mprZDklNklryFy2Q9Iv83wclzT/DbR4ws3ozq+/t7VV/f796e3vV09OjgYEBdXR0aHh4WC0tLcpms6em1fH9jo2Njcpms2ppadHw8LA6Ojo0MDCgnp4ejd9fZ2enhoaG1NraqvISp20rcisfO2tP/7p12ZhmlzptXJLV4llO6yuzuubK3J/1lbnLNi7Janap09ZlZ76PbSvGVF7idHd1VpUznDZUZbXyCqfr52V104Ksaiqcbl+U1dxyp801YyqRO7USM35MzY6Vucs314xpbnnu+jUVTjctyOr6ebn721CV1XtXjOnu6mxRbNPkPo2Ojqqpqem0Xo9/bW5uViaTUVtbmwYHB9XV1aW+vj719fWpq6tLg4ODamtrUyaTUXNz8xnvo6mpSaOjo2ptbdXQ0JA6OzsLnr1CbFNra2vRbVMx9qlQ27Rnz56i26Zi7FMht6mhoaHotqmQfTobc+7NPxrAzB6TVCOpVNI1krKSNjnnGszszyTtc8595Wy3X7dunauvr3/Tn3Gxlj/0ZEHv/1KaW+507GRxnM24c9e9oUu4LA0PD2vmzJmhy0Ag9B9k4PyYWYNzbt3ky8+5gjDJf0UAABr9SURBVOOc2+Gcu1XSdkkNkv5c0iYzK5F0h6TnfRebshvn81lUqTt06FDoEhAQ/QcZ8ONCDvj4oqR7JO2T9KRzrt1vSWnrHCqO1RtcuPnz37DXFwmh/yADfpRO9YrOuU5Jv5r/9raCVAMtmO7UxZCTtOPHj2vevHmhy0Ag9B9kwA/eshOZET6pIXklJfy3TBn9Bxnwg0cxMsOjrN6krqysLHQJCIj+gwz4wYATmaqZHGScuqGhodAlICD6DzLgBwNOZNoHWcFJ3cKFC0OXgIDoP8iAHww4kalbyApO6rq7u0OXgIDoP8iAHww4kXnuECs4qautrQ1dAgKi/yADfjDgRGbLMt5Glbr9+/eHLgEB0X+QAT8YcCLzjVemhS4Bga1ZsyZ0CQiI/oMM+MGAE5nxD8NEusY/cA5pov8gA34w4ERmdzsrOKmrq6sLXQICov8gA34w4ESGFRzw21va6D/IgB8MOJFhBQf89pY2+g8y4AcDTmS2LmMFJ3XNzc2hS0BA9B9kwA8GnMg83UNLUrdq1arQJSAg+g8y4AevppF5exVnMk5dV1dX6BIQEP0HGfCDAScyLx/jTMapu+qqq0KXgIDoP8iAHww4kamezQpO6o4dOxa6BARE/0EG/GDAiczgydAVILQZM2aELgEB0X+QAT8YcAAAQNFhwInMnPLQFSC0EydOhC4BAdF/kAE/GHAi0/06Bxmnbu7cuaFLQED0H2TADwacyFw3l4OMU3f48OHQJSAg+g8y4AcDTmR+0scKTupqampCl4CA6D/IgB8MOJHZtDQbugQEduDAgdAlICD6DzLgBwNOZL51kA/bTN0NN9wQugQERP9BBvxgwInMzlo+bDN1DQ0NoUtAQPQfZMAPBpzI7G5nBSd1dXV1oUtAQPQfZMAPBpzIsIIDfntLG/0HGfCDAScyrOCA397SRv9BBvxgwInMthWs4KSuqakpdAkIiP6DDPjBgBOZbx+kJalbvXp16BIQEP0HGfCDV9PIbFzCmYxT197eHroEBET/QQb8YMCJTEM/ZzJOXXV1degSEBD9BxnwgwEnMrVzWMFJXX9/f+gSEBD9BxnwgwEnMn3DrOCkrqKiInQJCIj+gwz4wYATmZmlrOCkbmRkJHQJCIj+gwz4wYATmTI6krxslg9cTRn9Bxnwg5fTyBzJsIsqdbNmzQpdAgKi/yADfjDgRGZ5BbuoUnf06NHQJSAg+g8y4AcDTmT2HWUFJ3VLliwJXQICov8gA34w4ETm1kXse03dK6+8EroEBET/QQb8YMCJzFNdtCR11157begSEBD9Bxnwg1fTyGxfyQpO6vbu3Ru6BARE/0EG/GDAicxjHdNCl4DAbr755tAlICD6DzLgBwNOZO6rHQtdAgJraGgIXQICov8gA34w4ETm0XZWcFJXV1cXugQERP9BBvxgwInMjpWs4KSusbExdAkIiP6DDPhxzgHHzErN7Btm9iMz+29mNsPMvmNmTWa228w4cYtHj3cwc6Zu7dq1oUtAQPQfZMCPqbya/itJTc65d0paLOkjkrqdc2skzZP07gLWl5x7angXVepaW1tDl4CA6D/IgB9TGXD+XtJ/NrNSSXMl3Szpe/l/e07SXQWqLUl7fs4KTupWrFgRugQERP9BBvw456upc27IOXdc0o8kHZa0QNIv8v88KGn+5NuY2QNmVm9m9b29verv71dvb696eno0MDCgjo4ODQ8Pq6WlRdls9tT+xvEjxxsbG5XNZtXS0qLh4WF1dHRoYGBAPT09Gr+/zs5ODQ0NqbW1VeUlTttW5I5d2Vl7+tety8Y0u9Rp45KsFs9yWl+Z1TVX5v6sr8xdtnFJVrNLnbYuO/N9bFsxpvISp7urs6qc4bShKquVVzhdPy+rmxZkVVPhdPuirOaWO22uGVOJ3KljacbfFbVjZe7yzTVjmlueu35NhdNNC7K6fl7u/jZUZfWOqqzurs4WxTZN7tPo6KiamppO6/X41+bmZmUyGbW1tWlwcFBdXV3q6+tTX1+furq6NDg4qLa2NmUyGTU3N5/xPpqamjQ6OqrW1lYNDQ2ps7Oz4NkrxDYdOnSo6LapGPtUqG1qbGwsum0qxj4Vcpt+9rOfFd02FbJPZ2POvfmHO5rZAklDkkaVW7GplfRR59w3zexjkuY75z5xttuvW7fO1dfXv+nPuFjLH3qyoPd/KdVUOHUNFcdhTZ277g1dwmVpYGBA8+bNC10GAqH/IAPnx8wanHPrJl8+lf0hH5O0zTk3Jum4pN+XtCn/bxslPe+tSmjBdD5NPHXHjx8PXQICov8gA35MZcD5c0kfMLMXJB2R9JeSlprZPklHJT1bwPqSM8IxxskrKeE4rJTRf5ABP0rPdQXnXI9yKzUTbS5MORgeLY7dU7hwZWVloUtAQPQfZMCPcw44uLSqZjp1vMaQk7KhoSEtXLgwdBkIhP6fv2I6DlOSNlRl9UJfcazihDwWszgewSLSPshwkzpe3NJG/8HrgB8MOJGpW8hBxqnr7u4OXQICov/gdcAPBpzIPHeIyT11tbW1oUtAQPQfvA74wYATmS3LeBtV6vbv3x+6BARE/8HrgB8MOJH5xivTQpeAwNasWRO6BARE/8HrgB8MOJEZ/ygFpGv8dOVIE/0HrwN+MOBEZnc7k3vq6urqQpeAgOg/eB3wgwEnMkzu4Df4tNF/8DrgBwNOZJjcwW/waaP/4HXADwacyGxdxuSeuubm5tAlICD6D14H/GDAiczTPbQkdatWrQpdAgKi/+B1wA8exci8vYozWKauq6srdAkIiP6D1wE/GHAi8/IxzmCZuquuuip0CQiI/oPXAT8YcCJTPZvJPXXHjh0LXQICov/gdcAPBpzIDJ4MXQFCmzFjRugSEBD9B68DfjDgAACAolMaugCcbk556AoQ2okTJ0KXcNlZ/tCToUvwZn1lVi+9Wjy/e3buujd0CZcdXgf8KJ7/RUWi+3UOLkvd3LlzQ5eAgHgOABnwgwEnMtfN5eCy1B0+fDh0CQiI5wCQAT8YcCLzkz4m99TV1NSELgEB8RwAMuAHA05kNi3Nhi4BgR04cCB0CQiI5wCQAT8YcCLzrYN8yFrqbrjhhtAlICCeA0AG/GDAiczOWj5kLXUNDQ2hS0BAPAeADPjBgBOZ3e1M7qmrq6sLXQIC4jkAZMAPBpzIMLmDFZy08RwAMuAHA05kmNzBCk7aeA4AGfCDAScy21YwuaeuqakpdAkIiOcAkAE/GHAi8+2DtCR1q1evDl0CAuI5AGTADx7FyGxcwhksU9fe3h66BATEcwDIgB8MOJFp6OcMlqmrrq4OXQIC4jkAZMAPBpzI1M5hck9df39/6BIQEM8BIAN+MOBEpm+YyT11FRUVoUtAQDwHgAz4URq6AJxuZqmTRLjPx/KHngxdglfXz8vqpwPF8btH5657Q5dw2eE5AGTAj+J4Fi0iZXQkeWQgbfQfZMAPHsbIHMkwtaeODKSN/oMM+MGAE5nlFRxcljoykDb6DzLgBwNOZPYdZXJPHRlIG/0HGfCDAScyty7Khi4BgZGBtNF/kAE/GHAi81QXLUkdGUgb/QcZ8INHMTLbVzK5p44MpI3+gwz4wYATmcc6poUuAYGRgbTRf5ABPxhwInNf7VjoEhAYGUgb/QcZ8IMBJzKPtjO5p44MpI3+gwz4wYATmR0rmdxTRwbSRv9BBvxgwInM4x20JHVkIG30H2TADx7FyNxTw9HzqSMDaaP/IAN+nHPAsZz/bmYvmtn/MLMKM/uOmTWZ2W4z45SLHu35OTNn6shA2ug/yIAfU3kU3ymp1Dl3i6Q5kj4gqds5t0bSPEnvLmB9yblxPp9BkjoykDb6DzLgx1QGnMOS/jT/95OSHpb0vfz3z0m6a/INzOwBM6s3s/re3l719/ert7dXPT09GhgYUEdHh4aHh9XS0qJsNqvGxkZJUkNDgySpsbFR2WxWLS0tGh4eVkdHhwYGBtTT06Px++vs7NTQ0JBaW1tVXuK0bUXuoKydtad/3bpsTLNLnTYuyWrxLKf1lVldc2Xuz/rK3GUbl2Q1u9Rp67Iz38e2FWMqL3G6uzqryhlOG6qyWnmF0/XzsrppQVY1FU63L8pqbrnT5poxlcidOkhs/O1+O1bmLt9cM6a55bnr11Q43bQgq+vn5e5vQ1VWx05Kd1dni2KbJvdpdHRUTU1Np/V6/Gtzc7MymYza2to0ODiorq4u9fX1qa+vT11dXRocHFRbW5symYyam5tPu22IPlXOcAXrU+eQFdU2Te51U1OTRkdH1draqqGhIXV2dl70c0SIPhUqe9WzXbDsFWKbzue5/EKfI2J63vPRJycVzTadz3P5hT5HnI05N7VJ0cy2Svq/JI1I+gPn3DNm9puS1jvnPnS2261bt87V19dP6WdcqOUPPVnQ+7+UblqQ1T8dKY7lyc5d916Sn1NM/ZfIwIUopgwUU/+lS5OBYuq/VFwZuBT9N7MG59y6yZdP6RE0s/9DueHmX0rqk3Rl/p+ulNTvq0hIIxxbljwykDb6DzLgx1QOMl4k6d9Lutc595qkZyVtyv/zRknPF6689AyPcsx26shA2ug/yIAfU1nB+Q1JiyV918z2SCqTtNTM9kk6qtzAA0+qZnJwWerIQNroP8iAH6XnuoJz7g8k/cGki/9rYcpB+yCTe+rIQNroP8iAH8VxFFMRqVvI5J46MpA2+g8y4AcDTmSeO8TknjoykDb6DzLgBwNOZLYs4/D51JGBtNF/kAE/GHAi841XpoUuAYGRgbTRf5ABPxhwIjN+Nkikiwykjf6DDPjBgBOZ3e1M7qkjA2mj/yADfjDgRIbJHWQgbfQfZMAPBpzIMLmDDKSN/oMM+MGAE5nxT4xFushA2ug/yIAfDDiRebqHlqSODKSN/oMM+MGjGJm3V3EGy9SRgbTRf5ABPxhwIvPyMc5gmToykDb6DzLgBwNOZKpnM7mnjgykjf6DDPjBgBOZwZOhK0BoZCBt9B9kwA8GHAAAUHQYcCIzpzx0BQiNDKSN/oMM+MGAE5nu1zm4LHVkIG30H2TADwacyFw3l4PLUkcG0kb/QQb8YMCJzE/6mNxTRwbSRv9BBvxgwInMpqXZ0CUgMDKQNvoPMuAHA05kvnWQD1lLHRlIG/0HGfCDAScyO2v5kLXUkYG00X+QAT8YcCKzu53JPXVkIG30H2TADwacyDC5gwykjf6DDPjBgBMZJneQgbTRf5ABPxhwIrNtBZN76shA2ug/yIAfDDiR+fZBWpI6MpA2+g8y4AePYmQ2LuEMlqkjA2mj/yADfjDgRKahnzNYpo4MpI3+gwz4wYATmdo5TO6pIwNpo/8gA34w4ESmb5jJPXVkIG30H2TADwacyMwsZXJPHRlIG/0HGfCDAScyZXQkeWQgbfQfZMAPHsbIHMmwNJk6MpA2+g8y4AcDTmSWV7A0mToykDb6DzLgBwNOZPYdZXJPHRlIG/0HGfCDAScyty7Khi4BgZGBtNF/kAE/GHAi81QXLUkdGUgb/QcZ8INHMTLbVzK5p44MpI3+gwz4wYATmcc6poUuAYGRgbTRf5ABPxhwInNf7VjoEhAYGUgb/QcZ8IMBJzKPtjO5p44MpI3+gwz4wYATmR0rmdxTRwbSRv9BBvxgwInM4x20JHVkIG30H2TADx7FyNxTw9HzqSMDaaP/IAN+MOBEZs/PaUnqyEDa6D/IgB9TehTNrMzM/mf+7zPM7Dtm1mRmu82Mc0p7dON8PoMkdWQgbfQfZMCPcw44ZjZTUoOkd+cvuk9St3NujaR5Ey6HB51DzIupIwNpo/8gA36cc8Bxzg07526U1J2/aKOk7+X//pykuwpUW5IWTGdyTx0ZSBv9Bxnw40J29C2Q9Iv83wclzZ98BTN7wMzqzay+t7dX/f396u3tVU9PjwYGBtTR0aHh4WG1tLQom82qsbFRktTQ0CBJamxsVDabVUtLi4aHh9XR0aGBgQH19PRo/P46Ozs1NDSk1tZWlZc4bVuRe1vdztrTv25dNqbZpU4bl2S1eJbT+sqsrrky92d9Ze6yjUuyml3qtHXZme9j24oxlZc43V2dVeUMpw1VWa28wun6eVndtCCrmgqn2xdlNbfcaXPNmErkTr3Nb/yETTtW5i7fXDOmueW569dUON20IKvr5+Xub0NVVjOn5X5OMWzT5D6Njo6qqanptF6Pf21ublYmk1FbW5sGBwfV1dWlvr4+9fX1qaurS4ODg2pra1Mmk1Fzc/Nptw3Rp8oZhevTSLa4tmlyr5uamjQ6OqrW1lYNDQ2ps7Pzop8jQvSpUNlbuyAbLHuF2KbzeS6/0OeImJ73fPSpaoYrmm06n+fyC32OOBtzbmqTopm1O+dqzezrkp5wzn3TzD4mab5z7hNnu926detcfX39lH7GhVr+0JMFvf9LaeUVTh2vFcfyZOeuey/Jzymm/ktk4EIUUwaKqf/SpclAMfVfKq4MXIr+m1mDc27d5MsvZAXnWUmb8n/fKOn5iykMp6uaydJk6shA2ug/yIAfFzLgfF3SUjPbJ+mocgMPPGkfLI6pHReODKSN/oMM+DHlAcc5V5v/mnHObXbO3eic2+mmuo8LU1K3kIczdWQgbfQfZMAPziYUmecOMbmnjgykjf6DDPjBgBOZLcs4RXfqyEDa6D/IgB8MOJH5xivTQpeAwMhA2ug/yIAfDDiRGT+XANJFBtJG/0EG/GDAiczudib31JGBtNF/kAE/GHAiw+QOMpA2+g8y4AcDTmSY3EEG0kb/QQb8YMCJzPjnjSBdZCBt9B9kwA8GnMg83UNLUkcG0kb/QQb84FGMzNurOINl6shA2ug/yIAfDDiRefkYZ7BMHRlIG/0HGfCDAScy1bOZ3FNHBtJG/0EG/GDAiczgydAVIDQykDb6DzLgBwMOAAAoOgw4kZlTHroChEYG0kb/QQb8YMCJTPfrHFyWOjKQNvoPMuAHA05krpvLwWWpIwNpo/8gA34w4ETmJ31M7qkjA2mj/yADfjDgRGbT0mzoEhAYGUgb/QcZ8IMBJzLfOsiHrKWODKSN/oMM+MGAE5mdtXzIWurIQNroP8iAHww4kdndzuSeOjKQNvoPMuAHA05kmNxBBtJG/0EG/GDAiQyTO8hA2ug/yIAfDDiR2baCyT11ZCBt9B9kwA8GnMh8+yAtSR0ZSBv9Bxnwg0cxMhuXcAbL1JGBtNF/kAE/GHAi09DPGSxTRwbSRv9BBvxgwIlM7Rwm99SRgbTRf5ABPxhwItM3zOSeOjKQNvoPMuAHA05kZpYyuaeODKSN/oMM+MGAE5kyOpI8MpA2+g8y4AcPY2SOZFiaTB0ZSBv9BxnwgwEnMssrWJpMHRlIG/0HGfCDAScy+44yuaeODKSN/oMM+MGAE5lbF2VDl4DAyEDa6D/IgB8MOJF5qouWpI4MpI3+gwz4waMYme0rmdxTRwbSRv9BBvxgwInMYx3TQpeAwMhA2ug/yIAfDDiRua92LHQJCIwMpI3+gwz4wYATmUfbmdxTRwbSRv9BBvxgwInMjpVM7qkjA2mj/yADfjDgRObxDlqSOjKQNvoPMuAHj2Jk7qnh6PnUkYG00X+QAT8YcCKz5+e0JHVkIG30H2TADx7FyNw4n88gSR0ZSBv9BxnwgwEnMp1DfAZJ6shA2ug/yIAf5z3gmNkMM/uOmTWZ2W4zoxMeLZjO5J46MpA2+g8y4MeFrODcJ6nbObdG0jxJ7/ZbUtpGOLYseWQgbfQfZMCPCxlwNkr6Xv7vz0m6y185GB5lQSx1ZCBt9B9kwA9z7vyWwszsu5L+0Dn3jJn9pqT1zrkPTbrOA5IeyH97jaSf+Sg2EQsl9YcuAkGRgbTRf5CB87PMOVc5+cLSC7ijfklX5v9+pc7QBOfcX0j6iwu47+SZWb1zbl3oOhAOGUgb/QcZ8ONCdlE9K2lT/u8bJT3vrxwAAICLdyEDztclLTWzfZKOKjfwAAAAROO8d1E55zKSNhegFuSwaw9kIG30H2TAg/M+yBgAACB2nMkYAAAUHQacS8TMykPXgLDIQNroP8jApXUhbxPHeTKz7codt3TfWf797yXNmHDRDEk/dc795iUoD5cAGUgb/QcZuPQYcArEzF6QNCwpK6lc0nwzeyb/z9Ml/YZz7p/z34855+6ccNvlkv79FH7GFZJ+LmmRc+41b8XDi0JnwMwelvTrkvokHZe0zTk36G8LcDEu0XPAf1LutB2HJf2fPA/E5RI9B+yQ9JqkJ5xzv++x/MseA06BOOc2SJKZ/Ymk/+ice83Mvizpz51z+yddfZqZPTjh+wWaWm82Kvef5i5J/8ND2fDoEmXgPznnHjWz/6DcE90jPmrHxSt0/83sHZJuk3SLpA8rd/b4P/JVPy7eJXoO+KykJyS9YGZ7nXNP+qi9GDDgFF6zpMX5fa/rJb1sZiWS5Jwb/0i1Typ3au5xnZK+PYX7vlvSn+e/MuDEq5AZGFcp6dDFl4oCKFT/3yPpKeecy+/eWO21avhU0OcA59xxM3tc0p2SGHDyeJt4gZjZhyT9tqSeM/xziaQ/lbRH0jcnXP6X+a8fnHDZNufcq2f5GS9LulXSD5xz11900fCq0BnIL0+/X9KQpLnKfS7csYuvHD5cgv4/IqneOfdVPxXDt0v0HNCeX8V9QNLbOGbnl1jBKZwrJf0/zrndZ7uCmVVK6nTO3W9mdyo32Zukh51z3zezr+W/P9NtV0lapNx/jKVm9hbnXJvnbcDFKWgG8n4//+T228rtnvjgm1wXl1ah+z8oqSJ/P2+TdIdz7g891o+LdymeA8bNlzRw8SUXD94mXjhvVW6J8c2cafkse47vx71HuU91v1PSF/LfIy6FzsBEv5B0xRSuh0un0P3/kX75//4u5Q5mRVwuyXOAmc2U9D5J35tyZQlgF1UB5N/d9JKktc65E29yvUrl9s22KreL4W8kjSl3sOgxSddKuuEsS5PfkfQ559yLZrZB0iecc3yERiQuUQYeVu5dVEeVW439LefcXr9bggtxifpvkr4o6e3KvYtqu3Pudc+bggt0CZ8Ddii3mveoc+5PPG/GZY0BpwDM7COSZjnnPn+O6y2StGvC0uRtyk3qP5qwNPkJ59yZ9t8iYmQgbfQfZCA8BpwCMbOSCUfHn+06Jqk8/wGm45eVK3c+hLFC14jCIgNpo/8gA2Ex4AAAgKLDQcYAAKDoMOAAAICiw4ADAACKDgMOAAAoOv8/Cx8nS2yMbP4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用「面向对象」的方法画图\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "# 设置标题和坐标轴标签\n",
    "ax.set_title(\"产品的销量变化\")\n",
    "\n",
    "# 画柱形图\n",
    "ax.bar(x, y, label='产品销量')\n",
    "\n",
    "# 设置网格线\n",
    "ax.grid(ls=':')\n",
    "\n",
    "# 设置图例的位置和大小\n",
    "ax.legend(fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3.7.0 64-bit",
   "language": "python",
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